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                <h1 id="apachecn"><center>ApacheCN</center></h1>
<p><center>代码: 让学习变得更简单</center></p>
<hr />
<h2 id="_1">组织介绍</h2>
<ul>
<li>首页地址:<ul>
<li><a href="http://www.apachecn.org">http://www.apachecn.org</a></li>
<li>资源汇总地址: <a href="https://apachecn.github.io/home">https://apachecn.github.io/home</a></li>
</ul>
</li>
<li>关于我们: <a href="https://apachecn.github.io/home/about">https://apachecn.github.io/home/about</a></li>
</ul>
<h2 id="_2">内容详情</h2>
<table>
  <tr>
    <th>模块</th>
    <th>章节</th>
    <th>类型</th>
    <th>负责人(GitHub)</th>
    <th>QQ</th>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/1.MLFoundation"> 第 1 章: 机器学习基础</a></td>
    <td>介绍</td>
    <td><a href="https://github.com/ElmaDavies">@毛红动</a></td>
    <td>1306014226</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/2.KNN">第 2 章: KNN 近邻算法</a></td>
    <td>分类</td>
    <td><a href="https://github.com/youyj521">@尤永江</a></td>
    <td>279393323</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/3.DecisionTree">第 3 章: 决策树</a></td>
    <td>分类</td>
    <td><a href="https://github.com/jingwangfei">@景涛</a></td>
    <td>844300439</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/4.NaiveBayesian">第 4 章: 朴素贝叶斯</a></td>
    <td>分类</td>
    <td><a href="https://github.com/wnma3mz">@wnma3mz</a><br/><a href="https://github.com/kailian">@分析</a></td>
    <td>1003324213<br/>244970749</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/5.Logistic">第 5 章: Logistic回归</a></td>
    <td>分类</td>
    <td><a href="https://github.com/DataMonk2017">@微光同尘</a></td>
    <td>529925688</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/6.SVM">第 6 章: SVM 支持向量机</a></td>
    <td>分类</td>
    <td><a href="https://github.com/VPrincekin">@王德红</a></td>
    <td>934969547</td>
  </tr>
  <tr>
    <td>网上组合内容</td>
    <td><a href="ml/7.Ensemble">第 7 章: 集成方法（随机森林和 AdaBoost）</a></td>
    <td>分类</td>
    <td><a href="https://github.com/jiangzhonglian">@片刻</a></td>
    <td>529815144</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/8.Regression">第 8 章: 回归</a></td>
    <td>回归</td>
    <td><a href="https://github.com/DataMonk2017">@微光同尘</a></td>
    <td>529925688</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/9.TreeRegression">第 9 章: 树回归</a></td>
    <td>回归</td>
    <td><a href="https://github.com/DataMonk2017">@微光同尘</a></td>
    <td>529925688</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/10.KMeans">第 10 章: K-Means 聚类</a></td>
    <td>聚类</td>
    <td><a href="https://github.com/xuzhaoqing">@徐昭清</a></td>
    <td>827106588</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/11.Apriori">第 11 章: 利用 Apriori 算法进行关联分析</a></td>
    <td>频繁项集</td>
    <td><a href="https://github.com/WindZQ">@刘海飞</a></td>
    <td>1049498972</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/12.FP-growth">第 12 章: FP-growth 高效发现频繁项集</a></td>
    <td>频繁项集</td>
    <td><a href="https://github.com/mikechengwei">@程威</a></td>
    <td>842725815</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/13.PCA">第 13 章: 利用 PCA 来简化数据</a></td>
    <td>工具</td>
    <td><a href="https://github.com/lljuan330">@廖立娟</a></td>
    <td>835670618</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/14.SVD">第 14 章: 利用 SVD 来简化数据</a></td>
    <td>工具</td>
    <td><a href="https://github.com/marsjhao">@张俊皓</a></td>
    <td>714974242</td>
  </tr>
  <tr>
    <td>机器学习实战</td>
    <td><a href="ml/15.BigData_MapReduce">第 15 章: 大数据与 MapReduce</a></td>
    <td>工具</td>
    <td>空缺 - 有兴趣私聊片刻</td>
    <td>842376188</td>
  </tr>
  <tr>
    <td>Ml项目实战</td>
    <td><a href="ml/16.RecommendedSystem">第 16 章: 推荐系统</a></td>
    <td>项目</td>
    <td>空缺 - 有兴趣私聊片刻</td>
    <td>842376188</td>
  </tr>
</table>
                
                  
                
              
              
                


              

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